ArticleSensors (Basel, Switzerland)2022
Simple and Robust Deep Learning Approach for Fast Fluorescence Lifetime Imaging.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Accurate and Fast Thermal Sensing via Phase-Responsive Nanothermometers and Neural Networks.Nano letters · 2025Article
- Phasor and neural network approaches for rapid fluorophore fraction analysis in temporal-spectral multiplexed data.Journal of biomedical optics · 2025Article
- The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.eLife · 2025Article
- Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging.Journal of the American Chemical Society · 2025Article
- The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.bioRxiv : the preprint server for biology · 2025Article
- Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.Sensors (Basel, Switzerland) · 2025Article
- Fluorescence lifetime imaging microscopy approach reveals quantitative signatures for hepatocellular carcinoma diagnosis.Frontiers in oncology · 2025Article
- Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method.Journal of biomedical optics · 2024Article
- Fluorescence Lifetime Measurements and Analyses: Protocols Using Flow Cytometry and High-Throughput Microscopy.Methods in molecular biology (Clifton, N.J.) · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixer for fast and robust FLIM analysis. The FLIM-MLP-Mixer has a simple network architecture yet a powerful learning ability from data. Compared with the traditional fitting and previously reported DL methods, the FLIM-MLP-Mixer shows superior performance in terms of accuracy and calculation speed, which has been validated using both synthetic and experimental data. All results indicate that our proposed method is well suited for accurately estimating lifetime parameters from measured fluorescence histograms, and it has great potential in various real-time FLIM applications.
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Registered trials
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